An application of machine learning to the motion response prediction of floating assets
This work addresses real-time motion prediction for floating assets in offshore engineering, offering improved accuracy for operational decision-making, though it is incremental as it applies existing machine learning techniques to a specific domain problem.
The study tackled the challenge of predicting floating offshore asset motion under stochastic conditions by developing a supervised machine learning model, which achieved mean prediction errors of less than 5% for mooring parameters and heading accuracy within 2.5 degrees, outperforming traditional methods.
The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.